Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied. We introduce Indic Jailbreak Robustness (IJR) a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (~2.09B speakers), covering 45,216 prompts in JSON (contract-bound) and Free (naturalistic) tracks.IJR reveals three patterns. (1) Contracts inflate refusals but do not stop jailbreaks: in JSON, LLaMA and Sarvam exceed 0.92 JSR, and in Free all models reach ~1.0 with refusals collapsing. (2) English→Indic attacks transfer strongly, with format wrappers often outperforming instruction wrappers. (3) Orthography matters: romanized/mixed inputs reduce JSR under JSON, with correlations to romanization share and tokenization ρ ≈ 0.28–0.32 indicating systematic effects. Human audits confirm detector reliability, and lite-to-full comparisons preserve conclusions. IJR offers a reproducible multilingual stress test revealing risks hidden by English-only, contract-focused evaluations, especially for South Asian users who frequently code-switch and romanize.

Clustering customer chat data is vital for cloud providers handling multi-service queries. Traditional methods struggle with overlapping concerns and create broad, static clusters that degrade over time. Re-clustering disrupts continuity, making issue tracking difficult. We propose an adaptive system that segments multi-turn chats into service-specific concerns and incrementally refines clusters as new issues arise. Cluster quality is tracked via Davies–Bouldin Index (DBI) and Silhouette Scores, with LLM-based splitting applied only to degraded clusters. Our method improves Silhouette Scores by over 100% and reduces DBI by 65.6% compared to baselines, enabling scalable, real-time analytics without full re-clustering.